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Laffafchi S, Ebrahimi A, Kafan S. Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data. Health Inf Sci Syst 2024; 12:17. [PMID: 38464464 PMCID: PMC10917730 DOI: 10.1007/s13755-024-00276-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 01/17/2024] [Indexed: 03/12/2024] Open
Abstract
Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.
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Affiliation(s)
- Soroor Laffafchi
- Department of Business Administration and Entrepreneurship, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Ahmad Ebrahimi
- Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Samira Kafan
- Department of Pulmonary Medicine, Sina Hospital, International Relations Office, Medical School, Tehran University of Medical Sciences, PourSina St., Tehran, 1417613151 Iran
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Alshehhi T, Ayesh A, Yang Y, Chen F. Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models1. Technol Health Care 2024:THC220598. [PMID: 38339943 DOI: 10.3233/thc-220598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2024]
Abstract
BACKGROUND The term 'dementia' covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency. OBJECTIVE This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer's disease (AD) - a common dementia condition. METHODS The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection. RESULTS A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository. CONCLUSION The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features.
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Huang G, Li R, Bai Q, Alty J. Multimodal learning of clinically accessible tests to aid diagnosis of neurodegenerative disorders: a scoping review. Health Inf Sci Syst 2023; 11:32. [PMID: 37489153 PMCID: PMC10363100 DOI: 10.1007/s13755-023-00231-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/04/2023] [Indexed: 07/26/2023] Open
Abstract
With ageing populations around the world, there is a rapid rise in the number of people with Alzheimer's disease (AD) and Parkinson's disease (PD), the two most common types of neurodegenerative disorders. There is an urgent need to find new ways of aiding early diagnosis of these conditions. Multimodal learning of clinically accessible data is a relatively new approach that holds great potential to support early precise diagnosis. This scoping review follows the PRSIMA guidelines and we analysed 46 papers, comprising 11,750 participants, 3569 with AD, 978 with PD, and 2482 healthy controls; the recency of this topic was highlighted by nearly all papers being published in the last 5 years. It highlights the effectiveness of combining different types of data, such as brain scans, cognitive scores, speech and language, gait, hand and eye movements, and genetic assessments for the early detection of AD and PD. The review also outlines the AI methods and the model used in each study, which includes feature extraction, feature selection, feature fusion, and using multi-source discriminative features for classification. The review identifies knowledge gaps around the need to validate findings and address limitations such as small sample sizes. Applying multimodal learning of clinically accessible tests holds strong potential to aid the development of low-cost, reliable, and non-invasive methods for early detection of AD and PD.
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Affiliation(s)
- Guan Huang
- School of ICT, University of Tasmania, Sandy Bay, TAS 7005 Australia
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, TAS 7000 Australia
| | - Renjie Li
- School of ICT, University of Tasmania, Sandy Bay, TAS 7005 Australia
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, TAS 7000 Australia
| | - Quan Bai
- School of ICT, University of Tasmania, Sandy Bay, TAS 7005 Australia
| | - Jane Alty
- Wicking Dementia Research and Education Centre, University of Tasmania, Hobart, TAS 7000 Australia
- School of Medicine, University of Tasmania, Hobart, TAS 7000 Australia
- Neurology Department, Royal Hobart Hospital, Hobart, 7000 Australia
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Shi M, Cheung G, Shahamiri SR. Speech and language processing with deep learning for dementia diagnosis: A systematic review. Psychiatry Res 2023; 329:115538. [PMID: 37864994 DOI: 10.1016/j.psychres.2023.115538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/23/2023]
Abstract
Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. Timely diagnosis of dementia could be followed by introducing interventions that may slow down its progression or reduce its burdens. However, the diagnostic process of dementia is often complex and resource intensive. Access to diagnostic services is also an issue in low and middle-income countries. The abundance and easy accessibility of speech and language data have created new possibilities for utilizing Deep Learning (DL) technologies to be part of the dementia diagnostic process. This systematic review included studies published between 2012-2022 that utilized such technologies to aid in diagnosing dementia. We identified 72 studies using the PRISMA 2020 protocol, extracted and analyzed data from these studies and reported the related DL technologies. We found these technologies effectively differentiated between healthy individuals and those with a dementia diagnosis, highlighting their potential in the diagnosis of dementia. This systematic review provides insights into the contributions of DL-based speech and language techniques to support the dementia diagnostic process. It also offers an understanding of the advancements made in this field thus far and highlights some challenges that still need to be addressed.
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Affiliation(s)
- Mengke Shi
- Department of Electrical, Computer and Software Engineering, Faculty of Engineering, University of Auckland, Private Bag 92019, Building 405, Level 6, Room 669, 3 Garfton Road, Auckland 1142, New Zealand
| | - Gary Cheung
- Department of Psychological Medicine, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Building 405, Level 6, Room 669, 3 Garfton Road, Auckland 1142, New Zealand
| | - Seyed Reza Shahamiri
- Department of Electrical, Computer and Software Engineering, Faculty of Engineering, University of Auckland, Private Bag 92019, Building 405, Level 6, Room 669, 3 Garfton Road, Auckland 1142, New Zealand.
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Shi Y, Ma Q, Feng C, Wang M, Wang H, Li B, Fang J, Ma S, Guo X, Li T. Microstate feature fusion for distinguishing AD from MCI. Health Inf Sci Syst 2022; 10:16. [PMID: 35911952 PMCID: PMC9325930 DOI: 10.1007/s13755-022-00186-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/12/2022] [Indexed: 11/25/2022] Open
Abstract
Electroencephalogram (EEG) microstates provide powerful tools for identifying EEG features due to their rich temporal information. In this study, we tested whether microstates can measure the severity of Alzheimer's disease (AD) and mild cognitive impairment (MCI) in patients and effectively distinguish AD from MCI. We defined two features using transition probabilities (TPs), and one was used to evaluate between-group differences in microstate parameters to assess the within-group consistency of TPs and MMSE scores. Another feature was used to distinguish AD from MCI in machine learning models. Tests showed that there were between-group differences in the temporal characteristics of microstates, and some kinds of TPs were significantly correlated with MMSE scores within groups. Based on our newly defined time-factor transition probabilities (TTPs) feature and partial accumulation strategy, we obtained promising scores for accuracy, sensitivity, and specificity of 0.938, 0.923, and 0.947, respectively. These results provide evidence for microstates as a neurobiological marker of AD.
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Affiliation(s)
- Yupan Shi
- Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, China
- Hebei Authentication Technology Engineering Research Center, Shijiazhuang, China
| | - Qinying Ma
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China
- Brain Aging and Cognitive Neuroscience Key Laboratory of Hebei Province, Shijiazhuang, China
| | - Chunyu Feng
- Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, China
- Hebei Authentication Technology Engineering Research Center, Shijiazhuang, China
| | - Mingwei Wang
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China
- Brain Aging and Cognitive Neuroscience Key Laboratory of Hebei Province, Shijiazhuang, China
| | - Hualong Wang
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China
- Brain Aging and Cognitive Neuroscience Key Laboratory of Hebei Province, Shijiazhuang, China
| | - Bing Li
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China
- Brain Aging and Cognitive Neuroscience Key Laboratory of Hebei Province, Shijiazhuang, China
| | - Jiyu Fang
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China
- Brain Aging and Cognitive Neuroscience Key Laboratory of Hebei Province, Shijiazhuang, China
| | - Shaochen Ma
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China
- Brain Aging and Cognitive Neuroscience Key Laboratory of Hebei Province, Shijiazhuang, China
| | - Xin Guo
- Department of Neurology, The First Hospital of Hebei Medical University, Shijiazhuang, China
- Brain Aging and Cognitive Neuroscience Key Laboratory of Hebei Province, Shijiazhuang, China
| | - Tongliang Li
- Institute of Applied Mathematics, Hebei Academy of Sciences, Shijiazhuang, China
- Institute of Biology, Hebei Academy of Sciences, Shijiazhuang, China
- Hebei Authentication Technology Engineering Research Center, Shijiazhuang, China
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The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines 2022; 10:biomedicines10020315. [PMID: 35203524 PMCID: PMC8869403 DOI: 10.3390/biomedicines10020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
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Dansson HV, Stempfle L, Egilsdóttir H, Schliep A, Portelius E, Blennow K, Zetterberg H, Johansson FD. Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer's disease. ALZHEIMERS RESEARCH & THERAPY 2021; 13:151. [PMID: 34488882 PMCID: PMC8422748 DOI: 10.1186/s13195-021-00886-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 08/08/2021] [Indexed: 11/10/2022]
Abstract
BACKGROUND In Alzheimer's disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology. METHODS A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts. RESULTS A β pathology status was determined based on the A β42/A β40 ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R2 score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R2 score of 0.228. CONCLUSION Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.
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Affiliation(s)
- Hákon Valur Dansson
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Lena Stempfle
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.
| | - Hildur Egilsdóttir
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Alexander Schliep
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
| | - Erik Portelius
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute, UCL, London, UK
| | - Fredrik D Johansson
- Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden
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Thabtah F, Peebles D, Retzler J, Hathurusingha C. Dementia medical screening using mobile applications: A systematic review with a new mapping model. J Biomed Inform 2020; 111:103573. [PMID: 32961306 DOI: 10.1016/j.jbi.2020.103573] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 09/13/2020] [Accepted: 09/14/2020] [Indexed: 12/19/2022]
Abstract
Early detection is the key to successfully tackling dementia, a neurocognitive condition common among the elderly. Therefore, screening using technological platforms such as mobile applications (apps) may provide an important opportunity to speed up the diagnosis process and improve accessibility. Due to the lack of research into dementia diagnosis and screening tools based on mobile apps, this systematic review aims to identify the available mobile-based dementia and mild cognitive impairment (MCI) apps using specific inclusion and exclusion criteria. More importantly, we critically analyse these tools in terms of their comprehensiveness, validity, performance, and the use of artificial intelligence (AI) techniques. The research findings suggest diagnosticians in a clinical setting use dementia screening apps such as ALZ and CognitiveExams since they cover most of the domains for the diagnosis of neurocognitive disorders. Further, apps such as Cognity and ACE-Mobile have great potential as they use machine learning (ML) and AI techniques, thus improving the accuracy of the outcome and the efficiency of the screening process. Lastly, there was overlapping among the dementia screening apps in terms of activities and questions they contain therefore mapping these apps to the designated cognitive domains is a challenging task, which has been done in this research.
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Affiliation(s)
- Fadi Thabtah
- Digital Technologies, Manukau Institute of Technology, Auckland, New Zealand.
| | - David Peebles
- Department of Psychology, University of Huddersfield, Huddersfield, UK.
| | - Jenny Retzler
- Department of Psychology, University of Huddersfield, Huddersfield, UK.
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